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Applications of Approximate Learning and Inference for Probabilistic Models

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Applications of Approximate Learning and Inference for Probabilistic Models

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Mean-Field methods for Structured Deep-Learning in Computer Vision

Learning embeddings: efficient algorithms and applications

Learning embeddings: efficient algorithms and applications

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Generative Models for Learning Robot Manipulation Skills from Humans

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

Real-time DCT Learning-based Reconstruction of Neural Signals

Enhancing Session-Based Recommendations through Sequential Modeling

Enhancing Session-Based Recommendations through Sequential Modeling

Learning embeddings: efficient algorithms and applications

Mean-Field methods for Structured Deep-Learning in Computer Vision

Learning embeddings: efficient algorithms and applications

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

Enhancing Session-Based Recommendations through Sequential Modeling

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Generative Models for Learning Robot Manipulation Skills from Humans

Real-time DCT Learning-based Reconstruction of Neural Signals

Enhancing Session-Based Recommendations through Sequential Modeling